Introduction to Linear Regression Analysis

Detalles Bibliográficos
Autor principal: Montgomery, Douglas C.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated, 2012.
Colección:New York Academy of Sciences Ser.
Temas:
Electronic books.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro
  • Contents
  • Series
  • Title Page
  • Copyright
  • PREFACE
  • CHAPTER 1: INTRODUCTION
  • 1.1 REGRESSION AND MODEL BUILDING
  • 1.2 DATA COLLECTION
  • 1.3 USES OF REGRESSION
  • 1.4 ROLE OF THE COMPUTER
  • CHAPTER 2: SIMPLE LINEAR REGRESSION
  • 2.1 SIMPLE LINEAR REGRESSION MODEL
  • 2.2 LEAST
  • SQUARES ESTIMATION OF THE PARAMETERS
  • 2.3 HYPOTHESIS TESTING ON THE SLOPE AND INTERCEPT
  • 2.4 INTERVAL ESTIMATION IN SIMPLE LINEAR REGRESSION
  • 2.5 PREDICTION OF NEW OBSERVATIONS
  • 2.6 COEFFICIENT OF DETERMINATION
  • 2.7 A SERVICE INDUSTRY APPLICATION OF REGRESSION
  • 2.8 USING SAS® AND R FOR SIMPLE LINEAR REGRESSION
  • 2.9 SOME CONSIDERATIONS IN THE USE OF REGRESSION
  • 2.10 REGRESSION THROUGH THE ORIGIN
  • 2.11 ESTIMATION BY MAXIMUM LIKELIHOOD
  • 2.12 CASE WHERE THE REGRESSOR X IS RANDOM
  • PROBLEMS
  • CHAPTER 3: MULTIPLE LINEAR REGRESSION
  • 3.1 MULTIPLE REGRESSION MODELS
  • 3.2 ESTIMATION OF THE MODEL PARAMETERS
  • 3.3 HYPOTHESIS TESTING IN MULTIPLE LINEAR REGRESSION
  • 3.4 CONFIDENCE INTERVALS IN MULTIPLE REGRESSION
  • 3.5 PREDICTION OF NEW OBSERVATIONS
  • 3.6 A MULTIPLE REGRESSION MODEL FOR THE PATIENT SATISFACTION DATA
  • 3.7 USING SAS AND R FOR BASIC MULTIPLE LINEAR REGRESSION
  • 3.8 HIDDEN EXTRAPOLATION IN MULTIPLE REGRESSION
  • 3.9 STANDARDIZED REGRESSION COEFFICIENTS
  • 3.10 MULTICOLLINEARITY
  • 3.11 WHY DO REGRESSION COEFFICIENTS HAVE THE WRONG SIGN?
  • PROBLEMS
  • CHAPTER 4: MODEL ADEQUACY CHECKING
  • 4.1 INTRODUCTION
  • 4.2 RESIDUAL ANALYSIS
  • 4.3 PRESS STATISTIC
  • 4.4 DETECTION AND TREATMENT OF OUTLIERS
  • 4.5 LACK OF FIT OF THE REGRESSION MODEL
  • PROBLEMS
  • CHAPTER 5: TRANSFORMATIONS AND WEIGHTING TO CORRECT MODEL INADEQUACIES
  • 5.1 INTRODUCTION
  • 5.2 VARIANCE
  • STABILIZING TRANSFORMATIONS
  • 5.3 TRANSFORMATIONS TO LINEARIZE THE MODEL
  • 5.4 ANALYTICAL METHODS FOR SELECTING A TRANSFORMATION
  • 5.5 GENERALIZED AND WEIGHTED LEAST SQUARES
  • 5.6 REGRESSION MODELS WITH RANDOM EFFECTS
  • PROBLEMS
  • CHAPTER 6: DIAGNOSTICS FOR LEVERAGE AND INFLUENCE
  • 6.1 IMPORTANCE OF DETECTING INFLUENTIAL OBSERVATIONS
  • 6.2 LEVERAGE
  • 6.3 MEASURES OF INFLUENCE: COOK'S D
  • 6.4 MEASURES OF INFLUENCE: DFFITS AND DFBETAS
  • 6.5 A MEASURE OF MODEL PERFORMANCE
  • 6.6 DETECTING GROUPS OF INFLUENTIAL OBSERVATIONS
  • 6.7 TREATMENT OF INFLUENTIAL OBSERVATIONS
  • PROBLEMS
  • CHAPTER 7: POLYNOMIAL REGRESSION MODELS
  • 7.1 INTRODUCTION
  • 7.2 POLYNOMIAL MODELS IN ONE VARIABLE
  • 7.3 NONPARAMETRIC REGRESSION
  • 7.4 POLYNOMIAL MODELS IN TWO OR MORE VARIABLES
  • 7.5 ORTHOGONAL POLYNOMIALS
  • PROBLEMS
  • CHAPTER 8: INDICATOR VARIABLES
  • 8.1 GENERAL CONCEPT OF INDICATOR VARIABLES
  • 8.2 COMMENTS ON THE USE OF INDICATOR VARIABLES
  • 8.3 REGRESSION APPROACH TO ANALYSIS OF VARIANCE
  • PROBLEMS
  • CHAPTER 9: MULTICOLLINEARITY
  • 9.1 INTRODUCTION
  • 9.2 SOURCES OF MULTICOLLINEARITY
  • 9.3 EFFECTS OF MULTICOLLINEARITY
  • 9.4 MULTICOLLINEARITY DIAGNOSTICS

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